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1849:, a centroid is chosen as the representative of all points. There are two approaches, the selection of the voxel centroid or select the centroid of the points lying within the voxel. To obtain internal points average has a higher computational cost, but offers better results. Thus, a subset of the input space is obtained that roughly represents the underlying surface. The Voxel Grid method presents the same problems as other filtering techniques: impossibility of defining the final number of points that represent the surface, geometric information loss due to the reduction of the points inside a voxel and sensitivity to noisy input spaces.
1783:, while other use a polyharmonic radial basis function is used to adjust the initial point set. Functions like Moving Least Squares, basic functions with local support, based on the Poisson equation have also been used. Loss of the geometry precision in areas with extreme curvature, i.e., corners, edges is one of the main issues encountered. Furthermore, pretreatment of information, by applying some kind of filtering technique, also affects the definition of the corners by softening them. There are several studies related to post-processing techniques used in the reconstruction for the
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of a two dimensional polygon and a three-dimensional polyhedron) which is neither convex nor necessarily connected. For a large value, the alpha-shape is identical to the convex-hull of S. The algorithm proposed by
Edelsbrunner and Mucke eliminates all tetrahedrons which are delimited by a surrounding sphere smaller than Ξ±. The surface is then obtained with the external triangles from the resulting tetrahedron.
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plane. This technique helps us to see comprehensively an entire compact structure of the object. Since the technique needs enormous amount of calculations, which requires strong configuration computers is appropriate for low contrast data. Two main methods for rays projecting can be considered as follows:
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Both methods have been recently extended for reconstructing point clouds with noise. In this method the quality of points determines the feasibility of the method. For precise triangulation since we are using the whole point cloud set, the points on the surface with the error above the threshold will
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Delaunay method involves extraction of tetrahedron surfaces from initial point cloud. The idea of βshapeβ for a set of points in space is given by concept of alpha-shapes. Given a finite point set S, and the real parameter alpha, the alpha-shape of S is a polytope (the generalization to any dimension
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The aim of feature extraction is to gain the characteristics of the images, through which the stereo correspondence processes. As a result, the characteristics of the images closely link to the choice of matching methods. There is no such universally applicable theory for features extraction, leading
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2D digital image acquisition is the information source of 3D reconstruction. Commonly used 3D reconstruction is based on two or more images, although it may employ only one image in some cases. There are various types of methods for image acquisition that depends on the occasions and purposes of the
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The research of 3D reconstruction has always been a difficult goal. By Using 3D reconstruction one can determine any object's 3D profile, as well as knowing the 3D coordinate of any point on the profile. The 3D reconstruction of objects is a generally scientific problem and core technology of a wide
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According to precise correspondence, combined with camera location parameters, 3D geometric information can be recovered without difficulties. Due to the fact that accuracy of 3D reconstruction depends on the precision of correspondence, error of camera location parameters and so on, the previous
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Entire volume transparence of the object is visualized using VR technique. Images will be performed by projecting rays through volume data. Along each ray, opacity and color need to be calculated at every voxel. Then information calculated along each ray will to be aggregated to a pixel on image
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Machine learning enables learning the correspondance between the subtle features in the input and the respective 3D equivalent. Deep neural networks have shown to be highly effective for 3D reconstruction from a single color image. This works even for non-photorealistic input images such as
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to observe one same object, acquiring two images from different points of view. In terms of trigonometry relations, depth information can be calculated from disparity. Binocular stereo vision method is well developed and stably contributes to favorable 3D reconstruction, leading to a better
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Image-order or ray-casting method: Projecting rays go through volume from front to back (from image plane to volume).There exists some other methods to composite image, appropriate methods depending on the user's purposes. Some usual methods in medical image are
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methods refer to using one or more images from one viewpoint (camera) to proceed to 3D construction. It makes use of 2D characteristics(e.g. Silhouettes, shading and texture) to measure 3D shape, and that's why it is also named Shape-From-X, where X can be
1771:. A contour algorithm is used to extracting a zero-set which is used to obtain polygonal representation of the object. Thus, the problem of reconstructing a surface from a disorganized point cloud is reduced to the definition of the appropriate function
399:, or by one single camera at different time in different viewing angles, are used to restore its 3D geometric information and reconstruct its 3D profile and location. This is more direct than Monocular methods such as shape-from-shading.
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are in the same direction with x-axis and y-axis of the camera's coordinate system respectively. The origin of the image's coordinate system is located on the intersection of imaging plane and the optical axis. Suppose such world point
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This approach is more sophisticated than the shape-of-shading method. Images taken in different lighting conditions are used to solve the depth information. It is worth mentioning that more than one image is required by this approach.
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Clinical routine of diagnosis, patient follow-up, computer assisted surgery, surgical planning etc. are facilitated by accurate 3D models of the desired part of human anatomy. Main motivation behind 3D reconstruction includes
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is the process of capturing the shape and appearance of real objects. This process can be accomplished either by active or passive methods. If the model is allowed to change its shape in time, this is referred to as
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Passive methods of 3D reconstruction do not interfere with the reconstructed object; they only use a sensor to measure the radiance reflected or emitted by the object's surface to infer its 3D structure through
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sketches. Thanks to the high level of accuracy in the reconstructed 3D features, deep learning based method has been employed for biomedical engineering applications to reconstruct CT imagery from X-ray.
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Mostly algorithms available for 3D reconstruction are extremely slow and cannot be used in real-time. Though the algorithms presented are still in infancy but they have the potential for fast computation.
197:, laser range finder and other active sensing techniques. A simple example of a mechanical method would use a depth gauge to measure a distance to a rotating object put on a turntable. More applicable
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specific application. Not only the requirements of the application must be met, but also the visual disparity, illumination, performance of camera and the feature of scenario should be considered.
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153:, etc. For instance, the lesion information of the patients can be presented in 3D on the computer, which offers a new and accurate approach in diagnosis and thus has vital clinical value.
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266:, texture etc. 3D reconstruction through monocular cues is simple and quick, and only one appropriate digital image is needed thus only one camera is adequate. Technically, it avoids
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of the camera's lens. However, to simplify the calculation, images are drawn in front of the optical center of the lens by f. The u-axis and v-axis of the image's coordinate system
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423:. The following picture provides a simple schematic diagram of horizontally sighted Binocular Stereo Vision, where b is the baseline between projective centers of two cameras.
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Helps in a number of clinical areas, such as radiotherapy planning and treatment verification, spinal surgery, hip replacement, neurointerventions and aortic stenting.
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performance when compared to other 3D construction. Unfortunately, it is computationally intensive, besides it performs rather poorly when baseline distance is large.
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approach and build the object in scenario based on model. These methods actively interfere with the reconstructed object, either mechanically or radiometrically using
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The origin of the camera's coordinate system is at the optical center of the camera's lens as shown in the figure. Actually, the camera's image plane is behind the
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Liping Zheng; Guangyao Li; Jing Sha (2007). "The survey of medical image 3D reconstruction". In Luo, Qingming; Wang, Lihong V.; Tuchin, Valery V.; Gu, Min (eds.).
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Another algorithm called Tight Cocone labels the initial tetrahedrons as interior and exterior. The triangles found in and out generate the resulting surface.
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Tracing a ray through a voxel grid. The voxels which are traversed in addition to those selected using a standard 8-connected algorithm are shown hatched.
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Corona-Figueroa, Abril; Bond-Taylor, Sam; Bhowmik, Neelanjan; Gaus, Yona
Falinie A.; Breckon, Toby P.; Shum, Hubert P. H.; Willcocks, Chris G. (2023).
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Wang, Jun; Gu, Dongxiao; Yu, Zeyun; Tan, Changbai; Zhou, Laishui (December 2012). "A framework for 3D model reconstruction in reverse engineering".
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Therefore, once the coordinates of image points is known, besides the parameters of two cameras, the 3D coordinate of the point can be determined.
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from two images. Certain interference factors in the scenario should be noticed, e.g. illumination, noise, surface physical characteristic, etc.
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Reconstruction of the surface is performed using a distance function which assigns to each point in the space a signed distance to the surface
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327:. Distortion and perspective measured in 2D images provide the hint for inversely solving depth of normal information of the object surface.
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Hoppe, Hugues; DeRose, Tony; Duchamp, Tom; McDonald, John; Stuetzle, Werner (July 1992). "Surface reconstruction from unorganized points".
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Feng, Qi; Shum, Hubert P. H.; Morishima, Shigeo (2022). "360 Depth
Estimation in the Wild - The Depth360 Dataset and the SegFuse Network".
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Mahmoudzadeh, Ahmadreza; Yeganeh, Sayna
Firoozi; Golroo, Amir (2019-07-09). "3D pavement surface reconstruction using an RGB-D sensor".
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of the camera. Visual disparity is defined as the difference in image point location of a certain world point acquired by two cameras,
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Camera calibration in
Binocular Stereo Vision refers to the determination of the mapping relationship between the image points
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Stereo vision obtains the 3-dimensional geometric information of an object from multiple images based on the research of human
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Lorensen, William E.; Cline, Harvey E. (July 1987). "Marching cubes: A high resolution 3D surface construction algorithm".
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Angelopoulou, A.; Psarrou, A.; Garcia-Rodriguez, J.; Orts-Escolano, S.; Azorin-Lopez, J.; Revett, K. (20 February 2015).
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towards the object and then measure its reflected part. Examples range from moving light sources, colored visible light,
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Suppose such an object with smooth surface covered by replicated texture units, and its projection from 3D to 2D causes
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in the 3D scenario. Camera calibration is a basic and essential part in 3D reconstruction via
Binocular Stereo Vision.
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In this filtering technique input space is sampled using a grid of 3D voxels to reduce the number of points. For each
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established the use of such methods. There are different variants for given algorithm, some use a discrete function
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2016:." International archives of photogrammetry remote sensing and spatial information sciences 34.3/W4 (2001): 37-44.
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Saxena, Ashutosh; Sun, Min; Ng, Andrew Y. (2007). "3-D Reconstruction from Sparse Views using
Monocular Vision".
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respectively on the left and right image plane. Assume two cameras are in the same plane, then y-coordinates of
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Carr, J.C.; Beatson, R.K.; Cherrie, J.B.; Mitchell, T.J.; Fright, W.R.; McCallum, B.C.; Evans, T.R. (2001).
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Stereo correspondence is to establish the correspondence between primitive factors in images, i.e. to match
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2610:"From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition"
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2207:"Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks"
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2044:"Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor"
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Synthesizing 3D Shapes via
Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks
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Mahmoudzadeh, Ahmadreza; Golroo, Amir; Jahanshahi, Mohammad R.; Firoozi
Yeganeh, Sayna (January 2019).
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Object-order method: Projecting rays go through volume from back to front (from volume to image plane).
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with a zero value for the sampled points and different to zero value for the rest. An algorithm called
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242:(one, two or more) or video. In this case we talk about image-based reconstruction and the output is a
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Can be used to plan, simulate, guide, or otherwise assist a surgeon in performing a medical procedure.
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2725:"3D reconstruction of medical images from slices automatically landmarked with growing neural models"
2644:." Proceedings of the 24th annual ACM symposium on User interface software and technology. ACM, 2011.
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Solid geometry with volume rendering Image courtesy of
Patrick Chris Fragile Ph.D., UC Santa Barbara
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McCoun, Jacques, and Lucien Reeves. Binocular vision: development, depth perception and disorders.
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Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers
246:. By comparison to active methods, passive methods can be applied to a wider range of situations.
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is an image sensor in a camera sensitive to visible light and the input to the method is a set of
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Shape from shading: A method for obtaining the shape of a smooth opaque object from one view
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Generating and reconstructing 3D shapes from single or multi-view depth maps or silhouettes
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Nozawa, Naoki; Shum, Hubert P. H.; Feng, Qi; Ho, Edmond S. L.; Morishima, Shigeo (2022).
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Please help update this article to reflect recent events or newly available information.
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3064:- Generate and reconstruct 3D shapes via modeling multi-view depth maps or silhouettes.
2829:; Goswami, Samrat (August 2006). "Probable surface reconstruction from noisy samples".
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Real time hand tracking and 3d gesture recognition for interactive interfaces using hmm
2697:." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
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Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry
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3D reconstruction of the general anatomy of the right side view of a small marine slug
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Wang, C.L. (June 2006). "Incremental reconstruction of sharp edges on mesh surfaces".
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Edelsbrunner, Herbert; MΓΌcke, Ernst (January 1994). "Three-dimensional alpha shapes".
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KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
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28th Annual Conference on Computer Graphics and Interactive Techniques SIGGRAPH 2001
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procedures must be done carefully to achieve relatively accurate 3D reconstruction.
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to a great diversity of stereo correspondence in Binocular Stereo Vision research.
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391:. The results are presented in form of depth maps. Images of an object acquired by
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Real-time 3D computed tomographic reconstruction using commodity graphics hardware
2956:"Implicit Surface Modelling with a Globally Regularised Basis of Compact Support"
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Poullis, Charalambos; You, Suya (May 2011). "3D Reconstruction of Urban Areas".
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The precise position and orientation of the patient's anatomy can be determined.
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2365:"3D Car Shape Reconstruction from a Contour Sketch using GAN and Lazy Learning"
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1984:
Fifth International Conference on Photonics and Imaging in Biology and Medicine
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1931:." Foundations and Trends in Computer Graphics and Vision 4.4 (2010): 287-404.
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2934:"Reconstruction and representation of 3d objects with radial basis functions"
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2239:"Photometric method for determining surface orientation from multiple images"
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Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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2521:." Exploring artificial intelligence in the new millennium 1.1-35 (2002): 1.
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Binocular stereo vision method requires two identical cameras with parallel
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http://research.microsoft.com/apps/search/default.aspx?q=3d+reconstruction
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Proceedings. 1984 IEEE International Conference on Robotics and Automation
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Soltani, A.A.; Huang, H.; Wu, J.; Kulkarni, T.D.; Tenenbaum, J.B. (2017).
1816:(maximum intensity projection), MinIP (minimum intensity projection), AC (
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2125:. Gool, Luc van., Vergauwen, Maarten. Hanover, MA: Now Publishers, Inc.
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to acquire object's 3D geometric information is on the basis of visual
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can be reconstructed using methods such as airborne laser altimetry or
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Analysis by synthesis: 3d object recognition by object reconstruction
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Due to the analysis of the shade information in the image, by using
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2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
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3D building model reconstruction from point clouds and ground plans
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3D reconstruction has applications in many fields. They include:
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http://www.nature.com/subjects/3d-reconstruction#news-and-comment
1986:. Proceedings of SPIE. Vol. 6534. pp. 65342Kβ65342Kβ6.
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http://6.869.csail.mit.edu/fa13/lectures/lecture11shapefromX.pdf
2684:." Computer vision and image understanding 81.3 (2001): 231-268.
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information of the object surface is restored to reconstruct.
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https://research.google.com/search.html#q=3D%20reconstruction
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Process of capturing the shape and appearance of real objects
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Kass, Michael; Witkin, Andrew; Terzopoulos, Demetri (1988).
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but these methods increase the complexity of the solution.
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variety of fields, such as Computer Aided Geometric Design (
2508:." ACM Transactions on Graphics. Vol. 22. No. 3. ACM, 2003.
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2007 IEEE 11th International Conference on Computer Vision
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3D reconstruction from multiple images. Part 1, Principles
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The 3D reconstruction consists of the following sections:
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3D reconstruction from multiple images part 1: Principles
2582:." Physics in Medicine & Biology 52.12 (2007): 3405.
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Real-time non-rigid reconstruction using an RGB-D camera
2288:"Recovering surface shape and orientation from texture"
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A survey of computer vision-based human motion capture
2598:." Journal of Cultural Heritage 15.3 (2014): 318-325.
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be explicitly represented on reconstructed geometry.
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Moons, Theo, Luc Van Gool, and Maarten Vergauwen. "
891:{\displaystyle v_{1}=v_{2}=f{\frac {y_{p}}{z_{p}}}}
181:Active methods, i.e. range data methods, given the
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1067:{\displaystyle d=u_{1}-u_{2}=f{\frac {b}{z_{p}}}}
2954:Walder, C.; SchΓΆlkopf, B.; Chapelle, O. (2006).
2608:Bruno, Fabio; et al. (JanuaryβMarch 2010).
1614:Improved accuracy due to multi view aggregation.
820:{\displaystyle u_{2}=f{\frac {x_{p}-b}{z_{p}}}}
2029:." Engineering geology 88.3-4 (2006): 173-199.
38:. For 3D reconstruction of sound sources, see
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2100:Buelthoff, Heinrich H., and Alan L. Yuille. "
1864:3D data acquisition and object reconstruction
754:{\displaystyle u_{1}=f{\frac {x_{p}}{z_{p}}}}
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2706:Keskin, Cem, Ayse Erkan, and Lale Akarun. "
2596:Learning cultural heritage by serious games
2102:Shape-from-X: Psychophysics and computation
361:It has been suggested that this section be
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2438:International Journal of Computer Vision
2213:. pp. 1511β1519 – via GitHub.
2025:Colesanti, Carlo, and Janusz Wasowski. "
2012:Vosselman, George, and Sander Dijkman. "
979:in the left camera's coordinate system,
289:" reconstructed from multiple viewpoints
2680:Moeslund, Thomas B., and Erik Granum. "
1977:
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3270:3D reconstruction from multiple images
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2656:Computers & Industrial Engineering
1869:3D reconstruction from multiple images
3290:Simultaneous localization and mapping
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1245:{\displaystyle z_{p}={\frac {bf}{d}}}
492:whose corresponding image points are
365:out into another article titled
7:
2710:." ICANN/ICONIPP 2003 (2003): 26-29.
2693:Hejrati, Mohsen, and Deva Ramanan. "
2506:Free-viewpoint video of human actors
1444:{\displaystyle P(x_{p},y_{p},z_{p})}
1785:detection and refinement of corners
1605:3D Reconstruction of medical images
952:{\displaystyle (x_{p},y_{p},z_{p})}
3355:Automatic number-plate recognition
1585:{\displaystyle P_{2}(u_{2},v_{2})}
1529:{\displaystyle P_{1}(u_{1},v_{1})}
1382:{\displaystyle P_{2}(u_{2},v_{2})}
1326:{\displaystyle P_{1}(u_{1},v_{1})}
1076:based on which the coordinates of
597:{\displaystyle P_{2}(u_{2},v_{2})}
541:{\displaystyle P_{1}(u_{1},v_{1})}
25:
430:Geometry of a stereoscopic system
3360:Automated species identification
415:The approach of using Binocular
352:
331:Machine Learning Based Solutions
185:, reconstruct the 3D profile by
49:
3345:Audio-visual speech recognition
3025:. Vol. 1. pp. 25β32.
2431:"Snakes: Active contour models"
3190:Recognition and categorization
2897:ACM SIGGRAPH Computer Graphics
2860:ACM SIGGRAPH Computer Graphics
2571:Xu, Fang, and Klaus Mueller. "
1579:
1553:
1523:
1497:
1438:
1399:
1376:
1350:
1320:
1294:
946:
907:
591:
565:
535:
509:
1:
3454:Optical character recognition
3385:Content-based image retrieval
3043:: CS1 maint: date and year (
2472:Nova Science Publishers, Inc.
1879:3D SEM surface reconstruction
2845:10.1016/j.comgeo.2005.10.006
2744:10.1016/j.neucom.2014.03.078
2628:10.1016/j.culher.2009.02.006
2615:Journal of Cultural Heritage
2307:10.1016/0004-3702(81)90019-9
1940:ZollhΓΆfer, Michael, et al. "
1273:Geometric camera calibration
411:Problem statement and basics
110:non-rigid or spatio-temporal
2237:Woodham, Robert J. (1980).
1955:"The Future of 3D Modeling"
1617:Detailed surface estimates.
691:{\displaystyle v_{1}=v_{2}}
270:, which is fairly complex.
177:map of an underwater canyon
116:Motivation and applications
3552:
3350:Automatic image annotation
3185:Noise reduction techniques
3031:10.1109/ROBOT.1984.1087212
2594:Mortara, Michela, et al. "
2382:10.1007/s00371-020-02024-y
2375:(4). Springer: 1317β1330.
2340:10.1109/VR51125.2022.00087
2324:. IEEE. pp. 664β673.
2286:Witkin, Andrew P. (1981).
1660:Tomographic reconstruction
1473:
1458:
1270:
393:two cameras simultaneously
341:
29:
3502:
3315:Free viewpoint television
3008:10.1016/j.cad.2006.02.009
2668:10.1016/j.cie.2012.07.009
2630:– via ResearchGate.
2519:Robotic mapping: A survey
2174:10.1109/ICCV.2007.4409219
1737:Delaunay and alpha-shapes
58:This article needs to be
30:For 3D reconstruction in
3380:Computer-aided diagnosis
2640:Izadi, Shahram, et al. "
2504:Carranza, Joel, et al. "
159:synthetic aperture radar
155:Digital elevation models
36:Iterative reconstruction
3442:Moving object detection
3432:Medical image computing
3195:Research infrastructure
3165:Image sensor technology
2541:10.1109/3dimpvt.2011.14
2295:Artificial Intelligence
1389:, and space coordinate
464:{\displaystyle O_{1}uv}
187:numerical approximation
40:3D sound reconstruction
3479:Video content analysis
3447:Small object detection
3226:Computer stereo vision
2943:. ACM. pp. 67β76.
2832:Computational Geometry
1837:
1795:
1759:
1733:
1730:Delaunay triangulation
1586:
1530:
1445:
1383:
1327:
1246:
1200:
1147:
1090:
1068:
993:
973:
953:
892:
821:
755:
692:
652:
625:
598:
542:
486:
465:
431:
368:Computer stereo vision
344:Computer stereo vision
298:Lambertian reflectance
290:
278:
250:Monocular cues methods
178:
92:
3484:Video motion analysis
3295:Structure from motion
3241:3D object recognition
2996:Computer-Aided Design
2919:10.1145/142920.134011
2802:10.1145/174462.156635
1909:Structure from motion
1835:
1793:
1757:
1728:
1698:3D object recognition
1587:
1531:
1470:Stereo correspondence
1446:
1384:
1328:
1247:
1201:
1148:
1091:
1069:
994:
974:
954:
893:
822:
756:
693:
653:
651:{\displaystyle P_{2}}
626:
624:{\displaystyle P_{1}}
599:
543:
487:
466:
429:
284:
276:
268:stereo correspondence
172:
143:computational science
84:
3526:3D computer graphics
3407:Foreground detection
3390:Reverse image search
3370:Bioimage informatics
3340:Activity recognition
2223:Horn, Berthold KP. "
2121:Moons, Theo (2010).
1824:(non-photorealistic
1721:Existing Approaches:
1668:Virtual environments
1648:video reconstruction
1639:Pavement engineering
1540:
1484:
1393:
1337:
1281:
1211:
1158:
1105:
1080:
1009:
983:
963:
904:
832:
766:
708:
662:
658:are identical, i.e.,
635:
608:
552:
496:
476:
442:
3474:Autonomous vehicles
3412:Gesture recognition
3275:2D to 3D conversion
2969:(3). Archived from
2882:10.1145/37402.37422
2794:1994math.....10208E
2517:Thrun, Sebastian. "
2258:1980OptEn..19..139W
2246:Optical Engineering
2060:2019Senso..19.1655M
1702:gesture recognition
1689:Reverse engineering
1096:can be worked out.
959:are coordinates of
232:image understanding
88:Pseudunela viatoris
3489:Video surveillance
3427:Landmark detection
3335:3D pose estimation
3320:Volumetric capture
3280:Gaussian splatting
3236:Object recognition
3150:Commercial systems
2578:2016-03-19 at the
2535:. pp. 33β40.
2450:10.1007/BF00133570
2266:10.1117/12.7972479
2107:2011-01-07 at the
1838:
1796:
1760:
1734:
1712:Problem Statement:
1582:
1526:
1476:Image registration
1461:Feature extraction
1455:Feature extraction
1441:
1379:
1323:
1267:Camera calibration
1242:
1196:
1143:
1086:
1064:
989:
969:
949:
888:
817:
751:
688:
648:
621:
594:
538:
482:
461:
432:
317:Shape-from-texture
309:Photometric Stereo
294:Shape-from-shading
291:
279:
221:for more details.
179:
131:computer animation
93:
3513:
3512:
3422:Image restoration
3365:Augmented reality
3330:
3329:
3310:4D reconstruction
3262:3D reconstruction
3155:Feature detection
2738:(Part A): 16β25.
2550:978-1-61284-429-9
2349:978-1-6654-9617-9
2183:978-1-4244-1630-1
2132:978-1-60198-285-8
2069:10.3390/s19071655
1992:10.1117/12.741321
1884:4D reconstruction
1818:alpha compositing
1685:Augmented reality
1677:Earth observation
1258:Image acquisition
1240:
1194:
1141:
1089:{\displaystyle P}
1062:
992:{\displaystyle f}
972:{\displaystyle P}
886:
815:
749:
485:{\displaystyle P}
385:
384:
380:
234:. Typically, the
127:computer graphics
105:3D reconstruction
101:computer graphics
79:
78:
16:(Redirected from
3543:
3437:Object detection
3402:Face recognition
3285:Shape from focus
3258:
3145:Digital geometry
3119:
3112:
3105:
3096:
3049:
3048:
3042:
3034:
3018:
3012:
3011:
2991:
2985:
2984:
2982:
2981:
2975:
2960:
2951:
2945:
2944:
2938:
2929:
2923:
2922:
2912:
2892:
2886:
2885:
2875:
2855:
2849:
2848:
2839:(1β2): 124β141.
2823:
2814:
2813:
2787:
2772:ACM Trans. Graph
2767:
2758:
2757:
2755:
2729:
2720:
2711:
2704:
2698:
2691:
2685:
2678:
2672:
2671:
2662:(4): 1189β1200.
2651:
2645:
2638:
2632:
2631:
2605:
2599:
2592:
2583:
2569:
2563:
2562:
2528:
2522:
2515:
2509:
2502:
2496:
2495:
2493:
2481:
2475:
2468:
2462:
2461:
2435:
2426:
2420:
2419:
2417:
2401:
2395:
2394:
2384:
2360:
2354:
2353:
2333:
2317:
2311:
2310:
2292:
2283:
2277:
2276:
2274:
2268:. Archived from
2243:
2234:
2228:
2221:
2215:
2214:
2202:
2196:
2195:
2167:
2158:. pp. 1β8.
2151:
2145:
2144:
2118:
2112:
2098:
2092:
2091:
2081:
2071:
2039:
2030:
2023:
2017:
2010:
2004:
2003:
1979:
1970:
1969:
1967:
1966:
1951:
1945:
1938:
1932:
1925:
1826:volume rendering
1763:Zero set Methods
1591:
1589:
1588:
1583:
1578:
1577:
1565:
1564:
1552:
1551:
1535:
1533:
1532:
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1522:
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1509:
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1423:
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1197:
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1149:
1144:
1142:
1137:
1136:
1135:
1122:
1117:
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1095:
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1087:
1073:
1071:
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1065:
1063:
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1040:
1039:
1027:
1026:
998:
996:
995:
990:
978:
976:
975:
970:
958:
956:
955:
950:
945:
944:
932:
931:
919:
918:
897:
895:
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884:
875:
874:
865:
857:
856:
844:
843:
826:
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813:
804:
797:
796:
786:
778:
777:
760:
758:
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750:
748:
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738:
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728:
720:
719:
697:
695:
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689:
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576:
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547:
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508:
507:
491:
489:
488:
483:
470:
468:
467:
462:
454:
453:
376:
356:
355:
348:
195:structured light
112:reconstruction.
74:
71:
65:
53:
52:
45:
21:
3551:
3550:
3546:
3545:
3544:
3542:
3541:
3540:
3536:Computer vision
3516:
3515:
3514:
3509:
3498:
3469:Robotic mapping
3417:Image denoising
3326:
3247:
3214:
3180:Motion analysis
3128:
3126:Computer vision
3123:
3071:
3058:
3053:
3052:
3035:
3020:
3019:
3015:
2993:
2992:
2988:
2979:
2977:
2973:
2958:
2953:
2952:
2948:
2936:
2931:
2930:
2926:
2894:
2893:
2889:
2857:
2856:
2852:
2825:
2824:
2817:
2769:
2768:
2761:
2727:
2722:
2721:
2714:
2705:
2701:
2692:
2688:
2679:
2675:
2653:
2652:
2648:
2639:
2635:
2607:
2606:
2602:
2593:
2586:
2580:Wayback Machine
2570:
2566:
2551:
2530:
2529:
2525:
2516:
2512:
2503:
2499:
2483:
2482:
2478:
2469:
2465:
2433:
2428:
2427:
2423:
2403:
2402:
2398:
2369:Visual Computer
2362:
2361:
2357:
2350:
2319:
2318:
2314:
2290:
2285:
2284:
2280:
2272:
2241:
2236:
2235:
2231:
2222:
2218:
2204:
2203:
2199:
2184:
2153:
2152:
2148:
2133:
2120:
2119:
2115:
2109:Wayback Machine
2099:
2095:
2041:
2040:
2033:
2024:
2020:
2011:
2007:
1981:
1980:
1973:
1964:
1962:
1953:
1952:
1948:
1939:
1935:
1926:
1922:
1917:
1855:
1672:virtual tourism
1652:Robotic mapping
1607:
1598:
1569:
1556:
1543:
1538:
1537:
1513:
1500:
1487:
1482:
1481:
1478:
1472:
1463:
1457:
1428:
1415:
1402:
1391:
1390:
1366:
1353:
1340:
1335:
1334:
1310:
1297:
1284:
1279:
1278:
1275:
1269:
1260:
1229:
1214:
1209:
1208:
1180:
1176:
1161:
1156:
1155:
1127:
1123:
1108:
1103:
1102:
1078:
1077:
1052:
1031:
1018:
1007:
1006:
981:
980:
961:
960:
936:
923:
910:
902:
901:
876:
866:
848:
835:
830:
829:
805:
788:
787:
769:
764:
763:
739:
729:
711:
706:
705:
698:. According to
678:
665:
660:
659:
638:
633:
632:
611:
606:
605:
581:
568:
555:
550:
549:
525:
512:
499:
494:
493:
474:
473:
445:
440:
439:
413:
381:
357:
353:
346:
340:
300:, the depth of
252:
227:
225:Passive methods
167:
147:virtual reality
139:medical imaging
135:computer vision
118:
97:computer vision
75:
69:
66:
63:
54:
50:
43:
32:medical imaging
28:
23:
22:
15:
12:
11:
5:
3549:
3547:
3539:
3538:
3533:
3528:
3518:
3517:
3511:
3510:
3503:
3500:
3499:
3497:
3496:
3494:Video tracking
3491:
3486:
3481:
3476:
3471:
3466:
3464:Remote sensing
3461:
3456:
3451:
3450:
3449:
3444:
3434:
3429:
3424:
3419:
3414:
3409:
3404:
3399:
3394:
3393:
3392:
3382:
3377:
3375:Blob detection
3372:
3367:
3362:
3357:
3352:
3347:
3342:
3337:
3331:
3328:
3327:
3325:
3324:
3323:
3322:
3317:
3307:
3302:
3300:View synthesis
3297:
3292:
3287:
3282:
3277:
3272:
3266:
3264:
3255:
3249:
3248:
3246:
3245:
3244:
3243:
3233:
3231:Motion capture
3228:
3222:
3220:
3216:
3215:
3213:
3212:
3207:
3202:
3197:
3192:
3187:
3182:
3177:
3172:
3167:
3162:
3157:
3152:
3147:
3142:
3136:
3134:
3130:
3129:
3124:
3122:
3121:
3114:
3107:
3099:
3093:
3092:
3087:
3082:
3077:
3070:
3069:External links
3067:
3066:
3065:
3057:
3056:External links
3054:
3051:
3050:
3013:
3002:(6): 689β702.
2986:
2946:
2924:
2887:
2873:10.1.1.545.613
2866:(4): 163β169.
2850:
2815:
2759:
2732:Neurocomputing
2712:
2699:
2686:
2673:
2646:
2633:
2600:
2584:
2564:
2549:
2523:
2510:
2497:
2476:
2463:
2444:(4): 321β331.
2421:
2396:
2355:
2348:
2312:
2301:(1β3): 17β45.
2278:
2275:on 2014-03-27.
2252:(1): 138β141.
2229:
2216:
2197:
2182:
2165:10.1.1.78.5303
2146:
2131:
2113:
2093:
2031:
2018:
2005:
1971:
1946:
1933:
1919:
1918:
1916:
1913:
1912:
1911:
1906:
1901:
1899:Photogrammetry
1896:
1891:
1886:
1881:
1876:
1871:
1866:
1861:
1854:
1851:
1830:
1829:
1809:
1777:marching cubes
1758:Marching Cubes
1748:
1747:
1744:
1709:
1708:
1695:
1693:Motion capture
1690:
1687:
1682:
1679:
1674:
1665:
1662:
1657:
1654:
1649:
1646:Free-viewpoint
1643:
1640:
1628:
1627:
1624:
1621:
1618:
1615:
1606:
1603:
1597:
1594:
1581:
1576:
1572:
1568:
1563:
1559:
1555:
1550:
1546:
1525:
1520:
1516:
1512:
1507:
1503:
1499:
1494:
1490:
1474:Main article:
1471:
1468:
1459:Main article:
1456:
1453:
1440:
1435:
1431:
1427:
1422:
1418:
1414:
1409:
1405:
1401:
1398:
1378:
1373:
1369:
1365:
1360:
1356:
1352:
1347:
1343:
1322:
1317:
1313:
1309:
1304:
1300:
1296:
1291:
1287:
1271:Main article:
1268:
1265:
1259:
1256:
1239:
1235:
1232:
1226:
1221:
1217:
1193:
1187:
1183:
1179:
1173:
1168:
1164:
1140:
1134:
1130:
1126:
1120:
1115:
1111:
1085:
1059:
1055:
1051:
1046:
1043:
1038:
1034:
1030:
1025:
1021:
1017:
1014:
988:
968:
948:
943:
939:
935:
930:
926:
922:
917:
913:
909:
883:
879:
873:
869:
863:
860:
855:
851:
847:
842:
838:
812:
808:
803:
800:
795:
791:
784:
781:
776:
772:
746:
742:
736:
732:
726:
723:
718:
714:
685:
681:
677:
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668:
645:
641:
618:
614:
593:
588:
584:
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575:
571:
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562:
558:
537:
532:
528:
524:
519:
515:
511:
506:
502:
481:
460:
457:
452:
448:
436:optical center
412:
409:
397:viewing angles
383:
382:
378:(October 2021)
360:
358:
351:
342:Main article:
339:
336:
255:Monocular cues
251:
248:
240:digital images
226:
223:
207:time-of-flight
166:
165:Active methods
163:
117:
114:
77:
76:
57:
55:
48:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
3548:
3537:
3534:
3532:
3529:
3527:
3524:
3523:
3521:
3508:
3507:
3506:Main category
3501:
3495:
3492:
3490:
3487:
3485:
3482:
3480:
3477:
3475:
3472:
3470:
3467:
3465:
3462:
3460:
3459:Pose tracking
3457:
3455:
3452:
3448:
3445:
3443:
3440:
3439:
3438:
3435:
3433:
3430:
3428:
3425:
3423:
3420:
3418:
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417:stereo vision
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395:in different
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3397:Eye tracking
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3253:Applications
3219:Technologies
3205:Segmentation
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2989:
2978:. Retrieved
2971:the original
2966:
2963:Eurographics
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2927:
2903:(2): 71β78.
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3200:Researchers
2753:10045/42544
2054:(7): 1655.
1904:Stereoscopy
1859:3D modeling
1732:(25 Points)
1681:Archaeology
1596:Restoration
702:relations,
325:perspective
287:visual hull
260:silhouettes
219:3D scanning
209:lasers to
199:radiometric
3531:3D imaging
3520:Categories
3175:Morphology
3133:Categories
2980:2018-10-09
2491:1907.04124
2415:2308.14152
2331:2202.08010
2227:." (1970).
1965:2017-05-27
1959:GarageFarm
1915:References
1874:3D scanner
1841:Voxel Grid
321:distortion
211:microwaves
18:3D mapping
3039:cite book
2905:CiteSeerX
2868:CiteSeerX
2391:1432-2315
2160:CiteSeerX
2141:607557354
1889:Depth map
1029:−
799:−
421:disparity
183:depth map
3210:Software
3170:Learning
3160:Geometry
3140:Datasets
2576:Archived
2458:12849354
2192:17571812
2105:Archived
2088:30959936
2000:62548928
1853:See also
1642:Medicine
244:3D model
203:radiance
2810:1600979
2790:Bibcode
2559:1189988
2474:, 2010.
2254:Bibcode
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2056:Bibcode
2048:Sensors
373:Discuss
264:shading
60:updated
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1996:S2CID
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